Reference for ultralytics/models/yolo/pose/predict.py

class PosePredictor(DetectionPredictor):
    """
    A class extending the DetectionPredictor class for prediction based on a pose model.

    Example:
        ```python
        from ultralytics.utils import ASSETS
        from ultralytics.models.yolo.pose import PosePredictor

        args = dict(model='yolov8n-pose.pt', source=ASSETS)
        predictor = PosePredictor(overrides=args)
        predictor.predict_cli()
        ```
    """

    def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
        super().__init__(cfg, overrides, _callbacks)
        self.args.task = 'pose'
        if isinstance(self.args.device, str) and self.args.device.lower() == 'mps':
            LOGGER.warning("WARNING ⚠️ Apple MPS known Pose bug. Recommend 'device=cpu' for Pose models. "
                           'See https://github.com/ultralytics/ultralytics/issues/4031.')

    def postprocess(self, preds, img, orig_imgs):
        """Return detection results for a given input image or list of images."""
        preds = ops.non_max_suppression(preds,
                                        self.args.conf,
                                        self.args.iou,
                                        agnostic=self.args.agnostic_nms,
                                        max_det=self.args.max_det,
                                        classes=self.args.classes,
                                        nc=len(self.model.names))

        if not isinstance(orig_imgs, list):  # input images are a torch.Tensor, not a list
            orig_imgs = ops.convert_torch2numpy_batch(orig_imgs)

        results = []
        for i, pred in enumerate(preds):
            orig_img = orig_imgs[i]
            pred[:, :4] = ops.scale_boxes(img.shape[2:], pred[:, :4], orig_img.shape).round()
            pred_kpts = pred[:, 6:].view(len(pred), *self.model.kpt_shape) if len(pred) else pred[:, 6:]
            pred_kpts = ops.scale_coords(img.shape[2:], pred_kpts, orig_img.shape)
            img_path = self.batch[0][i]
            results.append(
                Results(orig_img, path=img_path, names=self.model.names, boxes=pred[:, :6], keypoints=pred_kpts))
        return results